Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Constitutive and Regulated Gene Expression01:27

Constitutive and Regulated Gene Expression

1.8K
Gene expression in prokaryotes is governed by constitutive and regulated systems, allowing cells to balance the production of essential proteins with adaptive responses to environmental changes.Constitutive Gene ExpressionConstitutive, or housekeeping, genes are continuously expressed as they encode proteins vital for fundamental cellular processes. These include enzymes for glycolysis, ribosomal components for protein synthesis, and proteins involved in DNA replication. Their constant...
1.8K
Combinatorial Gene Control02:33

Combinatorial Gene Control

10.1K
Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
The expression of more than 30,000 genes is controlled by approximately 2000-3000 transcription factors. This is possible because a single transcription factor can recognize more than one regulatory sequence. The specificity in gene...
10.1K
Randomized Experiments01:13

Randomized Experiments

9.3K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
9.3K
Multiple Regression01:25

Multiple Regression

4.4K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
4.4K
Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

2.7K
2.7K
Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

7.5K
Transcriptional regulators bind to specific cis-regulatory sequences in the DNA to regulate gene transcription. These cis-regulatory sequences are very short, usually less than ten nucleotide pairs in length. The short length means that there is a high probability of the exact same sequence randomly occurring throughout the genome.  Since regulators can also bind to groups of similar sequences, this further increases the chances of random binding. Transcriptional regulators form...
7.5K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Cardiovascular-kidney-metabolic syndrome stage modifies the efficacy of intensive blood pressure control on cognitive outcomes: A post hoc analysis of SPRINT MIND.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026
Same author

A multi-task deep learning framework for simultaneous prediction of microsatellite instability and tumor mutational burden in gastric cancer from histopathological images.

Frontiers in oncology·2026
Same author

MCLAM: a cost-effective deep learning model for predicting recurrence risk in HR+/HER2- breast cancer-a multi-center study in a Chinese cohort.

Journal of the National Cancer Center·2026
Same author

Synergistic antioxidant and gene supplementation for high-efficacy retinitis pigmentosa therapy.

Science advances·2026
Same author

Characterization of telomere-related gene subtypes in lung adenocarcinoma and their implications for prognosis and treatment.

Discover oncology·2026
Same author

Deep Learning Can Predict the Overall Survival of Cervical Cancer Based on Histopathological Image, Gene Mutation and Clinical Information.

IET systems biology·2026

Related Experiment Video

Updated: Apr 10, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.7K

Integrative random forest for gene regulatory network inference.

Francesca Petralia1, Pei Wang1, Jialiang Yang1

  • 1Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.

Bioinformatics (Oxford, England)
|June 15, 2015
PubMed
Summary
This summary is machine-generated.

We developed iRafNet, a novel algorithm integrating diverse biological data to improve gene regulatory network (GRN) inference. This integrative random forest approach enhances accuracy and provides functional insights for GRNs.

More Related Videos

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

1.5K
Prediction and Validation of Gene Regulatory Elements Activated During Retinoic Acid Induced Embryonic Stem Cell Differentiation
09:07

Prediction and Validation of Gene Regulatory Elements Activated During Retinoic Acid Induced Embryonic Stem Cell Differentiation

Published on: June 21, 2016

8.8K

Related Experiment Videos

Last Updated: Apr 10, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.7K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

1.5K
Prediction and Validation of Gene Regulatory Elements Activated During Retinoic Acid Induced Embryonic Stem Cell Differentiation
09:07

Prediction and Validation of Gene Regulatory Elements Activated During Retinoic Acid Induced Embryonic Stem Cell Differentiation

Published on: June 21, 2016

8.8K

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Systems Biology

Background:

  • Gene regulatory network (GRN) inference is a key challenge in computational biology.
  • Integrating diverse biological data types can enhance GRN inference accuracy and power.
  • Existing methods may not fully leverage complementary information from heterogeneous data sources.

Purpose of the Study:

  • To propose iRafNet, a novel integrative algorithm for gene regulatory network inference.
  • To develop a flexible and unified framework for combining diverse biological data.
  • To improve the accuracy and functional insights of GRN inference.

Main Methods:

  • Developed iRafNet, an integrative random forest algorithm.
  • Integrated heterogeneous data including protein-protein interactions, TF-DNA-binding, and gene knock-down data.
  • Utilized data from DREAM4 and DREAM5 challenges for validation.

Main Results:

  • iRafNet demonstrated superior performance compared to GENIE3.
  • iRafNet achieved performance comparable to community learning approaches.
  • Application to Saccharomyces cerevisiae revealed improved TF-target gene regulation prediction and novel functional insights.

Conclusions:

  • iRafNet provides a powerful and flexible framework for integrative GRN inference.
  • The algorithm enhances the prediction of gene regulatory relationships.
  • iRafNet offers valuable functional insights into biological networks.